LIMN issue edited by Boris Jardine and Christopher Kelty: “Vast accumulations saturate our world: phone calls and emails stored by security agencies; every preference of every individual collected by advertisers; ID numbers, and maybe an iris scan, for every Indian; hundreds of thousands of whole genome sequences; seed banks of all existing plants, and of course, books… all of them. Just what is the purpose of these optimistically total archives, and how are they changing us?
This issue of Limn asks authors and artists to consider how these accumulations govern us, where this obsession with totality came from and how we might think differently about big data and algorithms, by thinking carefully through the figure of the archive.
Springwise: “The applications for virtual reality continue to grow — we have recently seen one VR game used to help recovering addicts and another that teaches peacekeeping skills. Now, the Aichi University of Technology has created a VR tsunami simulator, which can be experienced with Oculus Rift, Gear VR or Google Cardboard to help people prepare for natural disasters.
The three immersive videos — excerpts of which are on YouTube — were created by a team led by Dr. Tomoko Itamiya. They depict the effects of a tsunami similar to the one suffered by the country in 2011, in order that civilians can prepare themselves mentally for a natural disaster. Each video is in first person and guides the viewer through various stressful situations.
In one, the viewer is a driver, stuck in their car surrounded by water and floating vehicles. In another, the viewer is in a virtual flood, with water up to their knees and rising rapidly. All three videos use YouTube’s 360 degrees capability as well as sound effects to enhance the intensity of the situation. The hope is that by enabling viewers to experience the disaster in such an immersive way, they will be less prone to panic in the event of a real disaster….(More)”
Review by Iain Brassington of The Censor’s Hand: The Misregulation of Human-Subject Research (Carl E. Schneider, The MIT Press): “Scientific research can be a laborious and frustrating process even before it gets started—especially when it involves living human subjects. Universities and other research institutions maintain Institutional Review Boards that scrutinize research proposals and their methodologies, consent and privacy procedures, and so on. Similarly intensive reviews are required when the intention is to use human tissue—if, say, tissue from diagnostic cancer biopsies could potentially be used to gauge the prevalence of some other illness across the population. These procedures can generate absurdities. A doctor who wanted to know which television characters children recognized, for example, was advised to seek ethics committee approval, and told that he needed to do a pilot study as a precursor.
Today’s IRB system is the response to a historic problem: academic researchers’ tendency to behave abominably when left unmonitored. Nazi medical and pseudomedical experiments provide an obvious and well-known reference, but such horrors are not found only in totalitarian regimes. The Tuskegee syphilis study, for example, deliberately left black men untreated over the course of decades so researchers could study the natural course of the disease. On a much smaller but equally disturbing scale is the case of Dan Markingson, a 26-year-old University of Michigan graduate. Suffering from psychotic illness, Markingson was coercively enrolled in a study of antipsychotics to which he could not consent, and concerns about his deteriorating condition were ignored. In 2004, he was found dead, having almost decapitated himself with a box cutter.
Many thoughtful ethicists are aware of the imperfections of IRBs. They have worried publicly for some time that the IRB system, or parts of it, may claim an authority with which even many bioethicists are uncomfortable, and hinder science for no particularly good reason. Does the system need re-tuning, a total re-build, or something even more drastic?
When it comes to IRBs, Carl E. Schneider, a professor of law and internal medicine at the University of Michigan, belongs to the abolitionist camp. In The Censor’s Hand: The Misregulation of Human-Subject Research, he presents the case against the IRB system plainly. It is a case that rests on seven related charges.
IRBs, Schneider posits, cannot be shown to do good, with regulators able to produce “no direct evidence that IRBs prevented harm”; that an IRB at least went through the motions of reviewing the trial in which Markingson died might be cited as evidence of this. On top of that, he claims, IRBs sometimes cause harm, at least insofar as they slow down medical innovation. They are built to err on the side of caution, since “research on humans” can cover a vast range of activities and disciplines, and they struggle to take this range into proper account. Correspondingly, they “lack a legible and convincing ethics”; the autonomy of IRBs means that they come to different decisions on identical cases. (In one case, an IRB thought that providing supplemental vitamin A in a study was so dangerous that it should not be allowed; another thought that withholding it in the same study was so dangerous that it should not be allowed.) IRBs have unrealistically high expectations of their members, who are often fairly ad hoc groupings with no obvious relevant expertise. They overemphasize informed consent, with the unintended consequence that cramming every possible eventuality into a consent form makes it utterly incomprehensible. Finally, Schneider argues, IRBs corrode free expression by restricting what researchers can do and how they can do it….(More)”
Alison Powell at LSE Media Policy Project Blog: “Algorithms are everywhere, or so we are told, and the black boxes of algorithmic decision-making make oversight of processes that regulators and activists argue ought to be transparent more difficult than in the past. But when, and where, and which machines do we wish to make accountable, and for what purpose? In this post I discuss how algorithms discussed by scholars are most commonly those at work on media platforms whose main products are the social networks and attention of individuals. Algorithms, in this case, construct individual identities through patterns of behaviour, and provide the opportunity for finely targeted products and services. While there are serious concerns about, for instance, price discrimination, algorithmic systems for communicating and consuming are, in my view, less inherently problematic than processes that impact on our collective participation and belonging as citizenship. In this second sphere, algorithmic processes – especially machine learning – combine with processes of governance that focus on individual identity performance to profoundly transform how citizenship is understood and undertaken.
Communicating and consuming
In the communications sphere, algorithms are what makes it possible to make money from the web for example through advertising brokerage platforms that help companies bid for ads on major newspaper websites. IP address monitoring, which tracks clicks and web activity, creates detailed consumer profiles and transform the everyday experience of communication into a constantly-updated production of consumer information. This process of personal profiling is at the heart of many of the concerns about algorithmic accountability. The consequence of perpetual production of data by individuals and the increasing capacity to analyse it even when it doesn’t appear to relate has certainly revolutionalised advertising by allowing more precise targeting, but what has it done for areas of public interest?
John Cheney-Lippold identifies how the categories of identity are now developed algorithmically, since a category like gender is not based on self-discloure, but instead on patterns of behaviour that fit with expectations set by previous alignment to a norm. In assessing ‘algorithmic identities’, he notes that these produce identity profiles which are narrower and more behaviour-based than the identities that we perform. This is a result of the fact that many of the systems that inspired the design of algorithmic systems were based on using behaviour and other markers to optimise consumption. Algorithmic identity construction has spread from the world of marketing to the broader world of citizenship – as evidenced by the Citizen Ex experiment shown at the Web We Want Festival in 2015.
Individual consumer-citizens
What’s really at stake is that the expansion of algorithmic assessment of commercially derived big data has extended the frame of the individual consumer into all kinds of other areas of experience. In a supposed ‘age of austerity’ when governments believe it’s important to cut costs, this connects with the view of citizens as primarily consumers of services, and furthermore, with the idea that a citizen is an individual subject whose relation to a state can be disintermediated given enough technology. So, with sensors on your garbage bins you don’t need to even remember to take them out. With pothole reporting platforms like FixMyStreet, a city government can be responsive to an aggregate of individual reports. But what aspects of our citizenship are collective? When, in the algorithmic state, can we expect to be together?
Put another way, is there any algorithmic process to value the long term education, inclusion, and sustenance of a whole community for example through library services?…
Seeing algorithms – machine learning in particular – as supporting decision-making for broad collective benefit rather than as part of ever more specific individual targeting and segmentation might make them more accountable. But more importantly, this would help algorithms support society – not just individual consumers….(More)”
Ehud Shapiro in the Financial Times: “In 20 years, the internet has matured and has reached its equivalent of the Middle Ages. It has large feudal communities, with rulers who control everything and billions of serfs without civil rights. History tells us that the medieval era was followed by the Enlightenment. That great thinker of Enlightenment liberalism, John Stuart Mill, declared that there are three basic freedoms: freedom of thought and speech; freedom of “tastes and pursuits”; and the freedom to unite with others. The first two kinds of freedom are provided by the internet in abundance, at least in free countries.
But today’s internet technology does not support freedom of assembly, and consequently does not support democracy. For how can we practice democracy if people cannot assemble to discuss, take collective action or form political parties? The reason is that the internet currently is a masquerade. We can easily form a group on Google or Facebook, but we cannot know for sure who its members are. Online, people are sometimes not who they say they are.
Fortunately, help is on the way. The United Nations and the World Bank are committed to providing digital IDs to every person on the planet by 2030.
Digital IDs are smart cards that use public key cryptography, contain biometric information and allow easy proof of identity. They are already being used in many countries, but widespread use of them on the internet will require standardisation and seamless smartphone integration, which are yet to come.
In the meantime, we need to ask what kind of democracy could be realised on the internet. A new kind of online democracy is already emerging, with software such as Liquid Feedback or Adhocracy, which power “proposition development” and decision making. Known as “liquid” or “delegative democracy”, this is a hybrid of existing forms of direct and representative democracy.
It is like direct democracy, in that every vote is decided by the entire membership, directly or via delegation. It resembles representative democracy in that members normally trust delegates to vote on their behalf. But delegates must constantly earn the trust of the other members.
Another key question concerns which voting system to use. Systems that allow voters to rank alternatives are generally considered superior. Both delegative democracy and ranked voting require complex software and algorithms, and so previously were not practical. But they are uniquely suited to the internet.
Although today there are only a handful of efforts at internet democracy, I believe that smartphone-ready digital IDs will eventually usher in a “Cambrian explosion” of democratic forms. The resulting internet democracy will be far superior to its offline counterpart. Imagine a Facebook-like community that encompasses all of humanity. We may call it “united humanity”, as it will unite people, not nations. It will win hearts and minds by offering people the prospect of genuine participation, both locally and globally, in the democratic process….(More)
Paper by Rashmi Krishnamurthy andYukika Awazu in the International Journal of Information Management: “Public agencies around the globe are liberating their data. Drawing on a case of Data.gov, we outline the challenges and opportunities that lie ahead for the liberation of public data. Data.gov is an online portal that provides open access to datasets generated by US public agencies and countries around the world in a machine-readable format. By discussing the challenges and opportunities faced by Data.gov, we provide several lessons that can inform research and practice. We suggest that providing access to open data in itself does not spur innovation. Specifically, we claim that public agencies need to spend resources to improve the capacities of their organizations to move toward ‘open data by default’; develop capacities of community to use data to solve problems; and think critically about the unintended consequences of providing access to public data. We also suggest that public agencies need better metrics to evaluate the success of open-data efforts in achieving its goals….(More)”
Jessica Leber at Fast Co-Exist: “Cities have long seen the potential in big data to improve the government and the lives of citizens, and this is now being put into action in ways where governments touch citizens’ lives in very sensitive areas. New York City’s Department of Homelessness Services is mining apartment eviction filings, to see if they can understand who is at risk of becoming homeless and intervene early. And police departments all over the country have adopted predictive policing software that guides where officers should deploy, and at what time, leading to reduced crime in some cities.
In one study in Los Angeles, police officers deployed to certain neighborhoods by predictive policing software prevented 4.3 crimes per week, compared to 2 crimes per week when assigned to patrol a specific area by human crime analysts. Surely, a reduction in crime is a good thing. But community activists in places such as Bellingham, Washington, have grave doubts. They worry that outsiders can’t examine how the algorithms work, since the software is usually proprietary, and so citizens have no way of knowing what data the government is using to target them. They also worry that predictive policing is just exacerbating existing patterns of racial profiling. If the underlying crime data being used is the result of years of over-policing minority communities for minor offenses, then the predictions based on this biased data could create a feedback loop and lead to yet more over-policing.
At a smaller and more limited scale is the even more sensitive area of child protection services. Though the data isn’t really as “big” as in other examples, a few agencies are carefully exploring using statistical models to make decisions in several areas, such as which children in the system are most in danger of violence, which children are most in need of a trauma screening, and which are at risk of entering the criminal justice system.
In Hillsborough County, Florida, where a series of child homicides occurred, a private provider selected to manage the county’s child welfare system in 2012 came in and analyzed the data. Cases with the highest probability of serious injury or death had a few factors in common, they found: a child under the age of three, a “paramour” in the home, a substance abuse or domestic violence history, and a parent previously in the foster care system. They identified nine practices to use in these cases and hired a software provider to create a dashboard that allowed real-time feedback and dashboards. Their success has led to the program being implemented statewide….
“I think the opportunity is a rich one. At the same time, the ethical considerations need to be guiding us,” says Jesse Russell, chief program officer at the National Council on Crime and Delinquency, who has followed the use of predictive analytics in child protective services. Officials, he says, are treading carefully before using data to make decisions about individuals, especially when the consequences of being wrong—such as taking a child out of his or her home unnecessarily—are huge. And while caseworker decision-making can be flawed or biased, so can the programs that humans design. When you rely too much on data—if the data is flawed or incomplete, as could be the case in predictive policing—you risk further validating bad decisions or existing biases….
On the other hand, big data does have the potential to vastly expand our understanding of who we are and why we do what we do. A decade ago, serious scientists would have laughed someone out of the room who proposed a study of “the human condition.” It is a topic so broad and lacking in measurability. But perhaps the most important manifestation of big data in people’s lives could come from the ability for scientists to study huge, unwieldy questions they couldn’t before.
A massive scientific undertaking to study the human condition is set to launch in January of 2017. The Kavli Human Project, funded by the Kavli Foundation, plans to recruit 10,000 New Yorkers from all walks of life to be measured for 10 years. And by measured, they mean everything: all financial transactions, tax returns, GPS coordinates, genomes, chemical exposure, IQ, bluetooth sensors around the house, who subjects text and call—and that’s just the beginning. In all, the large team of academics expect to collect about a billion data points per person per year at an unprecedented low cost for each data point compared to other large research surveys.
The hope is with so much continuous data, researchers can for the first time start to disentangle the complex, seemingly unanswerable questions that have plagued our society, from what is causing the obesity epidemic to how to disrupt the poverty to prison cycle….(More)
Medha Basu at GovInsider Asia: “Countries across the region have launched complaints apps. But how do they fare? GovInsider takes a look at each how they compare.
#BetterPenang
#BetterPenang was one of the earliest complaints apps in the region, however. It was released in 2013, built by citizens with their own funding, and was later adopted by local authorities to respond to complaints. Officials answer to citizens directly on the app, in some cases responding with photos of how the problem was addressed.
The Mayor of Seberang Perai city in Penang monitors the complaints herself. She has appointed a team to ensure that complaints are responded to.
#BetterPenang compares with Jakarta’s similar Qlue app, which was built by a startup and is now being used by the city government.
Qlue has gone a step further with features to keep citizens engaged with the government. It ranks local authorities in Jakarta based on how quickly they respond to reports.
It has a gamified element that awards citizens points for posting complaints and inviting others to use the app. The points can be traded for avatars with “special powers”.
Inspired by #BetterPenang, the Malaysian federal government has launched its own complaints app, which it plans to push to every city in the country.
The features are similar to Penang’s app, but Cakna has broader coverage thanks to federal government backing. Reports are automatically sent to the relevant local authority in the country.
That also makes the app more comprehensive from the government’s perspective. The federal government can now monitor the quality of services across cities on an internal dashboard, while cities can see all of the complaints in their area.
Singapore’s OneService complaints app asks users to submit additional information, as compared to the other apps. It lets pick the date and time when problem was seen. This could help officials better track and resolve cases.
The government has created a separate unit – the Municipal Services Officer – to get these complaints addressed, particularly in areas that require coordination across agencies. The unit is already linked with at least 10 agencies, and is working to expanding this network to other agencies and town councils….(More)”
Emerging Technology from the arXiv: “Lack of evidence to city planning has ruined cities all over the world. But data-mining techniques are finally revealing the rules that make cities successful, vibrant places to live. …Back in 1961, the gradual decline of many city centers in the U.S. began to puzzle urban planners and activists alike. One of them, the urban sociologist Jane Jacobs, began a widespread and detailed investigation of the causes and published her conclusions in The Death and Life of Great American Cities, a controversial book that proposed four conditions that are essential for vibrant city life.
Jacobs’s conclusions have become hugely influential. Her ideas have had a significant impact on the development of many modern cities such as Toronto and New York City’s Greenwich Village. However, her ideas have also attracted criticism because of the lack of empirical evidence to back them up, a problem that is widespread in urban planning.
Today, that looks set to change thanks to the work of Marco De Nadai at the University of Trento and a few pals, who have developed a way to gather urban data that they use to test Jacobs’s conditions and how they relate to the vitality of city life. The new approach heralds a new age of city planning in which planners have an objective way of assessing city life and working out how it can be improved.
In her book, Jacobs argues that vibrant activity can only flourish in cities when the physical environment is diverse. This diversity, she says, requires four conditions. The first is that city districts must serve more than two functions so that they attract people with different purposes at different times of the day and night. Second, city blocks must be small with dense intersections that give pedestrians many opportunities to interact. The third condition is that buildings must be diverse in terms of age and form to support a mix of low-rent and high-rent tenants. By contrast, an area with exclusively new buildings can only attract businesses and tenants wealthy enough to support the cost of new building. Finally, a district must have a sufficient density of people and buildings.
While Jacobs’s arguments are persuasive, her critics say there is little evidence to show that these factors are linked with vibrant city life. That changed last year when urban scientists in Seoul, South Korea, published the result of a 10-year study of pedestrian activity in the city at unprecedented resolution. This work successfully tested Jacobs’s ideas for the first time.
However, the data was gathered largely through pedestrian surveys, a process that is time-consuming, costly, and generally impractical for use in most modern cities.
De Nadai and co have come up with a much cheaper and quicker alternative using a new generation of city databases and the way people use social media and mobile phones. The new databases include OpenStreetMap, the collaborative mapping tool; census data, which records populations and building use; land use data, which uses satellite images to classify land use according to various categories; Foursquare data, which records geographic details about personal activity; and mobile-phone records showing the number and frequency of calls in an area.
De Nadai and co gathered this data for six cities in Italy—Rome, Naples, Florence, Bologna, Milan, and Palermo.
Their analysis is straightforward. The team used mobile-phone activity as a measure of urban vitality and land-use records, census data, and Foursquare activity as a measure of urban diversity. Their goal was to see how vitality and diversity are correlated in the cities they studied. The results make for interesting reading….(More)
Stefaan Verhulst and Andrew Young at the GovLab: “Today, in “Open Data Impact: When Demand and Supply Meet,” the GovLab and Omidyar Network release key findings about the social, economic, cultural and political impact of open data. The findings are based on 19 detailed case studies of open data projects from around the world. These case studies were prepared in order to address an important shortcoming in our understanding of when, and how, open data works. While there is no shortage of enthusiasm for open data’s potential, nor of conjectural estimates of its hypothetical impact, few rigorous, systematic analyses exist of its concrete, real-world impact…. The 19 case studies that inform this report, all of which can be found at Open Data’s Impact (odimpact.org), a website specially set up for this project, were chosen for their geographic and sectoral representativeness. They seek to go beyond the descriptive (what happened) to the explanatory (why it happened, and what is the wider relevance or impact)….
In order to achieve the potential of open data and scale the impact of the individual projects discussed in our report, we need a better – and more granular – understanding of the enabling conditions that lead to success. We found 4 central conditions (“4Ps”) that play an important role in ensuring success:
Partnerships: Intermediaries and data collaboratives play an important role in ensuring success, allowing for enhanced matching of supply and demand of data.
Public infrastructure: Developing open data as a public infrastructure, open to all, enables wider participation, and a broader impact across issues and sectors.
Policies: Clear policies regarding open data, including those promoting regular assessments of open data projects, are also critical for success.
Problem definition: Open data initiatives that have a clear target or problem definition have more impact and are more likely to succeed than those with vaguely worded statements of intent or unclear reasons for existence.
Core Challenges
Finally, the success of a project is also determined by the obstacles and challenges it confronts. Our research uncovered 4 major challenges (“4Rs”) confronting open data initiatives across the globe:
Readiness: A lack of readiness or capacity (evident, for example, in low Internet penetration or technical literacy rates) can severely limit the impact of open data.
Responsiveness: Open data projects are significantly more likely to be successful when they remain agile and responsive—adapting, for instance, to user feedback or early indications of success and failure.
Risks: For all its potential, open data does pose certain risks, notably to privacy and security; a greater, more nuanced understanding of these risks will be necessary to address and mitigate them.
Resource Allocation: While open data projects can often be launched cheaply, those projects that receive generous, sustained and committed funding have a better chance of success over the medium and long term.
Toward a Next Generation Open Data Roadmap
The report we release today concludes with ten recommendations for policymakers, advocates, users, funders and other stakeholders in the open data community. For each step, we include a few concrete methods of implementation – ways to translate the broader recommendation into meaningful impact.
Together, these 10 recommendations and their means of implementation amount to what we call a “Next Generation Open Data Roadmap.” This roadmap is just a start, and we plan to continue fleshing it out in the near future. For now, it offers a way forward. It is our hope that this roadmap will help guide future research and experimentation so that we can continue to better understand how the potential of open data can be fulfilled across geographies, sectors and demographics.
Additional Resources
In conjunction with the release of our key findings paper, we also launch today an “Additional Resources” section on the Open Data’s Impact website. The goal of that section is to provide context on our case studies, and to point in the direction of other, complementary research. It includes the following elements:
A “repository of repositories,” including other compendiums of open data case studies and sources;
A compilation of some popular open data glossaries;
A number of open data research publications and reports, with a particular focus on impact;
A collection of open data definitions and a matrix of analysis to help assess those definitions….(More)
